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   "source": [
    "# Predict and Extract Features with Pre-trained Models\n",
    "\n",
    "This tutorial will work through how to use pre-trained models for predicting and feature extraction.\n",
    "\n",
    "## Download pre-trained models\n",
    "\n",
    "A model often contains two parts, the `.json` file specifying the neural network structure, and the `.params` file containing the binary parameters. The name convention is `name-symbol.json` and `name-epoch.params`, where `name` is the model name, and `epoch` is the epoch number. \n",
    "\n",
    "\n",
    "Here we download a pre-trained Resnet 50-layer model on Imagenet. Other models are available at http://data.mxnet.io/models/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os, urllib\n",
    "def download(url):\n",
    "    filename = url.split(\"/\")[-1]\n",
    "    if not os.path.exists(filename):\n",
    "        urllib.urlretrieve(url, filename)\n",
    "def get_model(prefix, epoch):\n",
    "    download(prefix+'-symbol.json')\n",
    "    download(prefix+'-%04d.params' % (epoch,))\n",
    "\n",
    "get_model('http://data.mxnet.io/models/imagenet/resnet/50-layers/resnet-50', 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialization\n",
    "\n",
    "We first load the model into memory with `load_checkpoint`. It returns the symbol (see [symbol.ipynb](../basic/symbol.ipynb)) definition of the neural network, and parameters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import mxnet as mx\n",
    "sym, arg_params, aux_params = mx.model.load_checkpoint('resnet-50', 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can visualize the neural network by `mx.viz.plot_network`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
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   "outputs": [
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       "<!-- softmax_label -->\n",
       "<g id=\"node278\" class=\"node\"><title>softmax_label</title>\n",
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       "<text text-anchor=\"middle\" x=\"1652\" y=\"-16005.3\" font-family=\"Times,serif\" font-size=\"14.00\">softmax_label</text>\n",
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       "<text text-anchor=\"middle\" x=\"1596\" y=\"-16099.3\" font-family=\"Times,serif\" font-size=\"14.00\">SoftmaxOutput</text>\n",
       "</g>\n",
       "<!-- softmax&#45;&gt;fc1 -->\n",
       "<g id=\"edge293\" class=\"edge\"><title>softmax&#45;&gt;fc1</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1573.57,-16065.1C1568.11,-16056.2 1562.36,-16046.7 1557.19,-16038.2\"/>\n",
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       "</g>\n",
       "<!-- softmax&#45;&gt;softmax_label -->\n",
       "<g id=\"edge294\" class=\"edge\"><title>softmax&#45;&gt;softmax_label</title>\n",
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       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.dot.Digraph at 0x7f3041ef1710>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mx.viz.plot_network(sym)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both argument parameters and auxiliary parameters (e.g mean/std in batch normalization layer) are stored as a dictionary of string name and ndarray value (see [ndarray.ipynb](../basic/ndarray.ipynb)). The arguments contain \n",
    "consist of weight and bias. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bn0_beta': <NDArray 64 @cpu(0)>,\n",
       " 'bn0_gamma': <NDArray 64 @cpu(0)>,\n",
       " 'bn1_beta': <NDArray 2048 @cpu(0)>,\n",
       " 'bn1_gamma': <NDArray 2048 @cpu(0)>,\n",
       " 'bn_data_beta': <NDArray 3 @cpu(0)>,\n",
       " 'bn_data_gamma': <NDArray 3 @cpu(0)>,\n",
       " 'conv0_weight': <NDArray 64x3x7x7 @cpu(0)>,\n",
       " 'fc1_bias': <NDArray 1000 @cpu(0)>,\n",
       " 'fc1_weight': <NDArray 1000x2048 @cpu(0)>,\n",
       " 'stage1_unit1_bn1_beta': <NDArray 64 @cpu(0)>,\n",
       " 'stage1_unit1_bn1_gamma': <NDArray 64 @cpu(0)>,\n",
       " 'stage1_unit1_bn2_beta': <NDArray 64 @cpu(0)>,\n",
       " 'stage1_unit1_bn2_gamma': <NDArray 64 @cpu(0)>,\n",
       " 'stage1_unit1_bn3_beta': <NDArray 64 @cpu(0)>,\n",
       " 'stage1_unit1_bn3_gamma': <NDArray 64 @cpu(0)>,\n",
       " 'stage1_unit1_conv1_weight': <NDArray 64x64x1x1 @cpu(0)>,\n",
       " 'stage1_unit1_conv2_weight': <NDArray 64x64x3x3 @cpu(0)>,\n",
       " 'stage1_unit1_conv3_weight': <NDArray 256x64x1x1 @cpu(0)>,\n",
       " 'stage1_unit1_sc_weight': <NDArray 256x64x1x1 @cpu(0)>,\n",
       " 'stage1_unit2_bn1_beta': <NDArray 256 @cpu(0)>,\n",
       " 'stage1_unit2_bn1_gamma': <NDArray 256 @cpu(0)>,\n",
       " 'stage1_unit2_bn2_beta': <NDArray 64 @cpu(0)>,\n",
       " 'stage1_unit2_bn2_gamma': <NDArray 64 @cpu(0)>,\n",
       " 'stage1_unit2_bn3_beta': <NDArray 64 @cpu(0)>,\n",
       " 'stage1_unit2_bn3_gamma': <NDArray 64 @cpu(0)>,\n",
       " 'stage1_unit2_conv1_weight': <NDArray 64x256x1x1 @cpu(0)>,\n",
       " 'stage1_unit2_conv2_weight': <NDArray 64x64x3x3 @cpu(0)>,\n",
       " 'stage1_unit2_conv3_weight': <NDArray 256x64x1x1 @cpu(0)>,\n",
       " 'stage1_unit3_bn1_beta': <NDArray 256 @cpu(0)>,\n",
       " 'stage1_unit3_bn1_gamma': <NDArray 256 @cpu(0)>,\n",
       " 'stage1_unit3_bn2_beta': <NDArray 64 @cpu(0)>,\n",
       " 'stage1_unit3_bn2_gamma': <NDArray 64 @cpu(0)>,\n",
       " 'stage1_unit3_bn3_beta': <NDArray 64 @cpu(0)>,\n",
       " 'stage1_unit3_bn3_gamma': <NDArray 64 @cpu(0)>,\n",
       " 'stage1_unit3_conv1_weight': <NDArray 64x256x1x1 @cpu(0)>,\n",
       " 'stage1_unit3_conv2_weight': <NDArray 64x64x3x3 @cpu(0)>,\n",
       " 'stage1_unit3_conv3_weight': <NDArray 256x64x1x1 @cpu(0)>,\n",
       " 'stage2_unit1_bn1_beta': <NDArray 256 @cpu(0)>,\n",
       " 'stage2_unit1_bn1_gamma': <NDArray 256 @cpu(0)>,\n",
       " 'stage2_unit1_bn2_beta': <NDArray 128 @cpu(0)>,\n",
       " 'stage2_unit1_bn2_gamma': <NDArray 128 @cpu(0)>,\n",
       " 'stage2_unit1_bn3_beta': <NDArray 128 @cpu(0)>,\n",
       " 'stage2_unit1_bn3_gamma': <NDArray 128 @cpu(0)>,\n",
       " 'stage2_unit1_conv1_weight': <NDArray 128x256x1x1 @cpu(0)>,\n",
       " 'stage2_unit1_conv2_weight': <NDArray 128x128x3x3 @cpu(0)>,\n",
       " 'stage2_unit1_conv3_weight': <NDArray 512x128x1x1 @cpu(0)>,\n",
       " 'stage2_unit1_sc_weight': <NDArray 512x256x1x1 @cpu(0)>,\n",
       " 'stage2_unit2_bn1_beta': <NDArray 512 @cpu(0)>,\n",
       " 'stage2_unit2_bn1_gamma': <NDArray 512 @cpu(0)>,\n",
       " 'stage2_unit2_bn2_beta': <NDArray 128 @cpu(0)>,\n",
       " 'stage2_unit2_bn2_gamma': <NDArray 128 @cpu(0)>,\n",
       " 'stage2_unit2_bn3_beta': <NDArray 128 @cpu(0)>,\n",
       " 'stage2_unit2_bn3_gamma': <NDArray 128 @cpu(0)>,\n",
       " 'stage2_unit2_conv1_weight': <NDArray 128x512x1x1 @cpu(0)>,\n",
       " 'stage2_unit2_conv2_weight': <NDArray 128x128x3x3 @cpu(0)>,\n",
       " 'stage2_unit2_conv3_weight': <NDArray 512x128x1x1 @cpu(0)>,\n",
       " 'stage2_unit3_bn1_beta': <NDArray 512 @cpu(0)>,\n",
       " 'stage2_unit3_bn1_gamma': <NDArray 512 @cpu(0)>,\n",
       " 'stage2_unit3_bn2_beta': <NDArray 128 @cpu(0)>,\n",
       " 'stage2_unit3_bn2_gamma': <NDArray 128 @cpu(0)>,\n",
       " 'stage2_unit3_bn3_beta': <NDArray 128 @cpu(0)>,\n",
       " 'stage2_unit3_bn3_gamma': <NDArray 128 @cpu(0)>,\n",
       " 'stage2_unit3_conv1_weight': <NDArray 128x512x1x1 @cpu(0)>,\n",
       " 'stage2_unit3_conv2_weight': <NDArray 128x128x3x3 @cpu(0)>,\n",
       " 'stage2_unit3_conv3_weight': <NDArray 512x128x1x1 @cpu(0)>,\n",
       " 'stage2_unit4_bn1_beta': <NDArray 512 @cpu(0)>,\n",
       " 'stage2_unit4_bn1_gamma': <NDArray 512 @cpu(0)>,\n",
       " 'stage2_unit4_bn2_beta': <NDArray 128 @cpu(0)>,\n",
       " 'stage2_unit4_bn2_gamma': <NDArray 128 @cpu(0)>,\n",
       " 'stage2_unit4_bn3_beta': <NDArray 128 @cpu(0)>,\n",
       " 'stage2_unit4_bn3_gamma': <NDArray 128 @cpu(0)>,\n",
       " 'stage2_unit4_conv1_weight': <NDArray 128x512x1x1 @cpu(0)>,\n",
       " 'stage2_unit4_conv2_weight': <NDArray 128x128x3x3 @cpu(0)>,\n",
       " 'stage2_unit4_conv3_weight': <NDArray 512x128x1x1 @cpu(0)>,\n",
       " 'stage3_unit1_bn1_beta': <NDArray 512 @cpu(0)>,\n",
       " 'stage3_unit1_bn1_gamma': <NDArray 512 @cpu(0)>,\n",
       " 'stage3_unit1_bn2_beta': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit1_bn2_gamma': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit1_bn3_beta': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit1_bn3_gamma': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit1_conv1_weight': <NDArray 256x512x1x1 @cpu(0)>,\n",
       " 'stage3_unit1_conv2_weight': <NDArray 256x256x3x3 @cpu(0)>,\n",
       " 'stage3_unit1_conv3_weight': <NDArray 1024x256x1x1 @cpu(0)>,\n",
       " 'stage3_unit1_sc_weight': <NDArray 1024x512x1x1 @cpu(0)>,\n",
       " 'stage3_unit2_bn1_beta': <NDArray 1024 @cpu(0)>,\n",
       " 'stage3_unit2_bn1_gamma': <NDArray 1024 @cpu(0)>,\n",
       " 'stage3_unit2_bn2_beta': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit2_bn2_gamma': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit2_bn3_beta': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit2_bn3_gamma': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit2_conv1_weight': <NDArray 256x1024x1x1 @cpu(0)>,\n",
       " 'stage3_unit2_conv2_weight': <NDArray 256x256x3x3 @cpu(0)>,\n",
       " 'stage3_unit2_conv3_weight': <NDArray 1024x256x1x1 @cpu(0)>,\n",
       " 'stage3_unit3_bn1_beta': <NDArray 1024 @cpu(0)>,\n",
       " 'stage3_unit3_bn1_gamma': <NDArray 1024 @cpu(0)>,\n",
       " 'stage3_unit3_bn2_beta': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit3_bn2_gamma': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit3_bn3_beta': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit3_bn3_gamma': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit3_conv1_weight': <NDArray 256x1024x1x1 @cpu(0)>,\n",
       " 'stage3_unit3_conv2_weight': <NDArray 256x256x3x3 @cpu(0)>,\n",
       " 'stage3_unit3_conv3_weight': <NDArray 1024x256x1x1 @cpu(0)>,\n",
       " 'stage3_unit4_bn1_beta': <NDArray 1024 @cpu(0)>,\n",
       " 'stage3_unit4_bn1_gamma': <NDArray 1024 @cpu(0)>,\n",
       " 'stage3_unit4_bn2_beta': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit4_bn2_gamma': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit4_bn3_beta': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit4_bn3_gamma': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit4_conv1_weight': <NDArray 256x1024x1x1 @cpu(0)>,\n",
       " 'stage3_unit4_conv2_weight': <NDArray 256x256x3x3 @cpu(0)>,\n",
       " 'stage3_unit4_conv3_weight': <NDArray 1024x256x1x1 @cpu(0)>,\n",
       " 'stage3_unit5_bn1_beta': <NDArray 1024 @cpu(0)>,\n",
       " 'stage3_unit5_bn1_gamma': <NDArray 1024 @cpu(0)>,\n",
       " 'stage3_unit5_bn2_beta': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit5_bn2_gamma': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit5_bn3_beta': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit5_bn3_gamma': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit5_conv1_weight': <NDArray 256x1024x1x1 @cpu(0)>,\n",
       " 'stage3_unit5_conv2_weight': <NDArray 256x256x3x3 @cpu(0)>,\n",
       " 'stage3_unit5_conv3_weight': <NDArray 1024x256x1x1 @cpu(0)>,\n",
       " 'stage3_unit6_bn1_beta': <NDArray 1024 @cpu(0)>,\n",
       " 'stage3_unit6_bn1_gamma': <NDArray 1024 @cpu(0)>,\n",
       " 'stage3_unit6_bn2_beta': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit6_bn2_gamma': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit6_bn3_beta': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit6_bn3_gamma': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit6_conv1_weight': <NDArray 256x1024x1x1 @cpu(0)>,\n",
       " 'stage3_unit6_conv2_weight': <NDArray 256x256x3x3 @cpu(0)>,\n",
       " 'stage3_unit6_conv3_weight': <NDArray 1024x256x1x1 @cpu(0)>,\n",
       " 'stage4_unit1_bn1_beta': <NDArray 1024 @cpu(0)>,\n",
       " 'stage4_unit1_bn1_gamma': <NDArray 1024 @cpu(0)>,\n",
       " 'stage4_unit1_bn2_beta': <NDArray 512 @cpu(0)>,\n",
       " 'stage4_unit1_bn2_gamma': <NDArray 512 @cpu(0)>,\n",
       " 'stage4_unit1_bn3_beta': <NDArray 512 @cpu(0)>,\n",
       " 'stage4_unit1_bn3_gamma': <NDArray 512 @cpu(0)>,\n",
       " 'stage4_unit1_conv1_weight': <NDArray 512x1024x1x1 @cpu(0)>,\n",
       " 'stage4_unit1_conv2_weight': <NDArray 512x512x3x3 @cpu(0)>,\n",
       " 'stage4_unit1_conv3_weight': <NDArray 2048x512x1x1 @cpu(0)>,\n",
       " 'stage4_unit1_sc_weight': <NDArray 2048x1024x1x1 @cpu(0)>,\n",
       " 'stage4_unit2_bn1_beta': <NDArray 2048 @cpu(0)>,\n",
       " 'stage4_unit2_bn1_gamma': <NDArray 2048 @cpu(0)>,\n",
       " 'stage4_unit2_bn2_beta': <NDArray 512 @cpu(0)>,\n",
       " 'stage4_unit2_bn2_gamma': <NDArray 512 @cpu(0)>,\n",
       " 'stage4_unit2_bn3_beta': <NDArray 512 @cpu(0)>,\n",
       " 'stage4_unit2_bn3_gamma': <NDArray 512 @cpu(0)>,\n",
       " 'stage4_unit2_conv1_weight': <NDArray 512x2048x1x1 @cpu(0)>,\n",
       " 'stage4_unit2_conv2_weight': <NDArray 512x512x3x3 @cpu(0)>,\n",
       " 'stage4_unit2_conv3_weight': <NDArray 2048x512x1x1 @cpu(0)>,\n",
       " 'stage4_unit3_bn1_beta': <NDArray 2048 @cpu(0)>,\n",
       " 'stage4_unit3_bn1_gamma': <NDArray 2048 @cpu(0)>,\n",
       " 'stage4_unit3_bn2_beta': <NDArray 512 @cpu(0)>,\n",
       " 'stage4_unit3_bn2_gamma': <NDArray 512 @cpu(0)>,\n",
       " 'stage4_unit3_bn3_beta': <NDArray 512 @cpu(0)>,\n",
       " 'stage4_unit3_bn3_gamma': <NDArray 512 @cpu(0)>,\n",
       " 'stage4_unit3_conv1_weight': <NDArray 512x2048x1x1 @cpu(0)>,\n",
       " 'stage4_unit3_conv2_weight': <NDArray 512x512x3x3 @cpu(0)>,\n",
       " 'stage4_unit3_conv3_weight': <NDArray 2048x512x1x1 @cpu(0)>}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arg_params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "while auxiliaries contains the the mean and std for the batch normalization layers.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bn0_moving_mean': <NDArray 64 @cpu(0)>,\n",
       " 'bn0_moving_var': <NDArray 64 @cpu(0)>,\n",
       " 'bn1_moving_mean': <NDArray 2048 @cpu(0)>,\n",
       " 'bn1_moving_var': <NDArray 2048 @cpu(0)>,\n",
       " 'bn_data_moving_mean': <NDArray 3 @cpu(0)>,\n",
       " 'bn_data_moving_var': <NDArray 3 @cpu(0)>,\n",
       " 'stage1_unit1_bn1_moving_mean': <NDArray 64 @cpu(0)>,\n",
       " 'stage1_unit1_bn1_moving_var': <NDArray 64 @cpu(0)>,\n",
       " 'stage1_unit1_bn2_moving_mean': <NDArray 64 @cpu(0)>,\n",
       " 'stage1_unit1_bn2_moving_var': <NDArray 64 @cpu(0)>,\n",
       " 'stage1_unit1_bn3_moving_mean': <NDArray 64 @cpu(0)>,\n",
       " 'stage1_unit1_bn3_moving_var': <NDArray 64 @cpu(0)>,\n",
       " 'stage1_unit2_bn1_moving_mean': <NDArray 256 @cpu(0)>,\n",
       " 'stage1_unit2_bn1_moving_var': <NDArray 256 @cpu(0)>,\n",
       " 'stage1_unit2_bn2_moving_mean': <NDArray 64 @cpu(0)>,\n",
       " 'stage1_unit2_bn2_moving_var': <NDArray 64 @cpu(0)>,\n",
       " 'stage1_unit2_bn3_moving_mean': <NDArray 64 @cpu(0)>,\n",
       " 'stage1_unit2_bn3_moving_var': <NDArray 64 @cpu(0)>,\n",
       " 'stage1_unit3_bn1_moving_mean': <NDArray 256 @cpu(0)>,\n",
       " 'stage1_unit3_bn1_moving_var': <NDArray 256 @cpu(0)>,\n",
       " 'stage1_unit3_bn2_moving_mean': <NDArray 64 @cpu(0)>,\n",
       " 'stage1_unit3_bn2_moving_var': <NDArray 64 @cpu(0)>,\n",
       " 'stage1_unit3_bn3_moving_mean': <NDArray 64 @cpu(0)>,\n",
       " 'stage1_unit3_bn3_moving_var': <NDArray 64 @cpu(0)>,\n",
       " 'stage2_unit1_bn1_moving_mean': <NDArray 256 @cpu(0)>,\n",
       " 'stage2_unit1_bn1_moving_var': <NDArray 256 @cpu(0)>,\n",
       " 'stage2_unit1_bn2_moving_mean': <NDArray 128 @cpu(0)>,\n",
       " 'stage2_unit1_bn2_moving_var': <NDArray 128 @cpu(0)>,\n",
       " 'stage2_unit1_bn3_moving_mean': <NDArray 128 @cpu(0)>,\n",
       " 'stage2_unit1_bn3_moving_var': <NDArray 128 @cpu(0)>,\n",
       " 'stage2_unit2_bn1_moving_mean': <NDArray 512 @cpu(0)>,\n",
       " 'stage2_unit2_bn1_moving_var': <NDArray 512 @cpu(0)>,\n",
       " 'stage2_unit2_bn2_moving_mean': <NDArray 128 @cpu(0)>,\n",
       " 'stage2_unit2_bn2_moving_var': <NDArray 128 @cpu(0)>,\n",
       " 'stage2_unit2_bn3_moving_mean': <NDArray 128 @cpu(0)>,\n",
       " 'stage2_unit2_bn3_moving_var': <NDArray 128 @cpu(0)>,\n",
       " 'stage2_unit3_bn1_moving_mean': <NDArray 512 @cpu(0)>,\n",
       " 'stage2_unit3_bn1_moving_var': <NDArray 512 @cpu(0)>,\n",
       " 'stage2_unit3_bn2_moving_mean': <NDArray 128 @cpu(0)>,\n",
       " 'stage2_unit3_bn2_moving_var': <NDArray 128 @cpu(0)>,\n",
       " 'stage2_unit3_bn3_moving_mean': <NDArray 128 @cpu(0)>,\n",
       " 'stage2_unit3_bn3_moving_var': <NDArray 128 @cpu(0)>,\n",
       " 'stage2_unit4_bn1_moving_mean': <NDArray 512 @cpu(0)>,\n",
       " 'stage2_unit4_bn1_moving_var': <NDArray 512 @cpu(0)>,\n",
       " 'stage2_unit4_bn2_moving_mean': <NDArray 128 @cpu(0)>,\n",
       " 'stage2_unit4_bn2_moving_var': <NDArray 128 @cpu(0)>,\n",
       " 'stage2_unit4_bn3_moving_mean': <NDArray 128 @cpu(0)>,\n",
       " 'stage2_unit4_bn3_moving_var': <NDArray 128 @cpu(0)>,\n",
       " 'stage3_unit1_bn1_moving_mean': <NDArray 512 @cpu(0)>,\n",
       " 'stage3_unit1_bn1_moving_var': <NDArray 512 @cpu(0)>,\n",
       " 'stage3_unit1_bn2_moving_mean': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit1_bn2_moving_var': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit1_bn3_moving_mean': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit1_bn3_moving_var': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit2_bn1_moving_mean': <NDArray 1024 @cpu(0)>,\n",
       " 'stage3_unit2_bn1_moving_var': <NDArray 1024 @cpu(0)>,\n",
       " 'stage3_unit2_bn2_moving_mean': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit2_bn2_moving_var': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit2_bn3_moving_mean': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit2_bn3_moving_var': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit3_bn1_moving_mean': <NDArray 1024 @cpu(0)>,\n",
       " 'stage3_unit3_bn1_moving_var': <NDArray 1024 @cpu(0)>,\n",
       " 'stage3_unit3_bn2_moving_mean': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit3_bn2_moving_var': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit3_bn3_moving_mean': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit3_bn3_moving_var': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit4_bn1_moving_mean': <NDArray 1024 @cpu(0)>,\n",
       " 'stage3_unit4_bn1_moving_var': <NDArray 1024 @cpu(0)>,\n",
       " 'stage3_unit4_bn2_moving_mean': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit4_bn2_moving_var': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit4_bn3_moving_mean': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit4_bn3_moving_var': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit5_bn1_moving_mean': <NDArray 1024 @cpu(0)>,\n",
       " 'stage3_unit5_bn1_moving_var': <NDArray 1024 @cpu(0)>,\n",
       " 'stage3_unit5_bn2_moving_mean': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit5_bn2_moving_var': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit5_bn3_moving_mean': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit5_bn3_moving_var': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit6_bn1_moving_mean': <NDArray 1024 @cpu(0)>,\n",
       " 'stage3_unit6_bn1_moving_var': <NDArray 1024 @cpu(0)>,\n",
       " 'stage3_unit6_bn2_moving_mean': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit6_bn2_moving_var': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit6_bn3_moving_mean': <NDArray 256 @cpu(0)>,\n",
       " 'stage3_unit6_bn3_moving_var': <NDArray 256 @cpu(0)>,\n",
       " 'stage4_unit1_bn1_moving_mean': <NDArray 1024 @cpu(0)>,\n",
       " 'stage4_unit1_bn1_moving_var': <NDArray 1024 @cpu(0)>,\n",
       " 'stage4_unit1_bn2_moving_mean': <NDArray 512 @cpu(0)>,\n",
       " 'stage4_unit1_bn2_moving_var': <NDArray 512 @cpu(0)>,\n",
       " 'stage4_unit1_bn3_moving_mean': <NDArray 512 @cpu(0)>,\n",
       " 'stage4_unit1_bn3_moving_var': <NDArray 512 @cpu(0)>,\n",
       " 'stage4_unit2_bn1_moving_mean': <NDArray 2048 @cpu(0)>,\n",
       " 'stage4_unit2_bn1_moving_var': <NDArray 2048 @cpu(0)>,\n",
       " 'stage4_unit2_bn2_moving_mean': <NDArray 512 @cpu(0)>,\n",
       " 'stage4_unit2_bn2_moving_var': <NDArray 512 @cpu(0)>,\n",
       " 'stage4_unit2_bn3_moving_mean': <NDArray 512 @cpu(0)>,\n",
       " 'stage4_unit2_bn3_moving_var': <NDArray 512 @cpu(0)>,\n",
       " 'stage4_unit3_bn1_moving_mean': <NDArray 2048 @cpu(0)>,\n",
       " 'stage4_unit3_bn1_moving_var': <NDArray 2048 @cpu(0)>,\n",
       " 'stage4_unit3_bn2_moving_mean': <NDArray 512 @cpu(0)>,\n",
       " 'stage4_unit3_bn2_moving_var': <NDArray 512 @cpu(0)>,\n",
       " 'stage4_unit3_bn3_moving_mean': <NDArray 512 @cpu(0)>,\n",
       " 'stage4_unit3_bn3_moving_var': <NDArray 512 @cpu(0)>}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aux_params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we create an executable `module` (see [module.ipynb](../basic/module.ipynb)) on GPU 0. To use a difference device, we just need to charge the context, e.g. `mx.cpu()` for CPU and `mx.gpu(2)` for the 3rd GPU. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mod = mx.mod.Module(symbol=sym, context=mx.gpu())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ResNet is trained with RGB images of size 224 x 224. The training data is feed by the variable `data`. We bind the module with the input shape and specify that it is only for predicting. The number 1 added before the image shape (3x224x224) means that we will only predict one image each time. Next we set the loaded parameters. Now the module is ready to run. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mod.bind(for_training = False,\n",
    "         data_shapes=[('data', (1,3,224,224))])\n",
    "mod.set_params(arg_params, aux_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare data\n",
    "\n",
    "We first obtain the synset file, in which the i-th line contains the label for the i-th class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "download('http://data.mxnet.io/models/imagenet/resnet/synset.txt')\n",
    "with open('synset.txt') as f:\n",
    "    synsets = [l.rstrip() for l in f]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We next download 1000 images for testing, which were not used for the training. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import tarfile\n",
    "download('http://data.mxnet.io/data/val_1000.tar')\n",
    "tfile = tarfile.open('val_1000.tar')\n",
    "tfile.extractall()\n",
    "with open('val_1000/label') as f:\n",
    "    val_label = [int(l.split('\\t')[0]) for l in f]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the first 8 images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ntzLBBh5w3DDLlOA0wE4PpRWxFNaCOLfItFZog5NiIYVAT2I9B5at8AyxKah1YhZDHJ0t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\n70Fa5RauhEIuTxwFxGHEcrMF2hDEUXY6m8YYNNFcnWajQZxqpBSsWb36e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0U0lheYn01Q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\nbYJEq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PIb0oyHhy3JkENkNrx6gCGfSG+Qh4zZ2JX43v0+3zeFRBAEGKk5cbrHRC3nJZfM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\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qieqgLvkfpJ6MNrS2L5lVjQn5MjMn7Aml0ZlGmCbv/9Av/K+tSJxeDjlw9BmqX32Qn/iD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\n9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OhHg0VjUhTiCYepS+aFRtYldZFjgow66eLNRiSdJTwsp3u7xN0OSZwSJQlVWaA6y0hr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\nlULpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3041dc8310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "matplotlib.rc(\"savefig\", dpi=100)\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "for i in range(0,8):\n",
    "    img = cv2.cvtColor(cv2.imread('val_1000/%d.jpg' % (i,)), cv2.COLOR_BGR2RGB)\n",
    "    plt.subplot(2,4,i+1)\n",
    "    plt.imshow(img)\n",
    "    plt.axis('off')\n",
    "    label = synsets[val_label[i]]\n",
    "    label = ' '.join(label.split(',')[0].split(' ')[1:])\n",
    "    plt.title(label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we define a function that reads one image each time and convert to a format can be used by the model. Here we use a naive way that resizes the original image into the desired shape, and change the data layout. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "def get_image(filename):\n",
    "    img = cv2.imread(filename)  # read image in b,g,r order\n",
    "    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)   # change to r,g,b order\n",
    "    img = cv2.resize(img, (224, 224))  # resize to 224*224 to fit model\n",
    "    img = np.swapaxes(img, 0, 2)\n",
    "    img = np.swapaxes(img, 1, 2)  # change to (channel, height, width)\n",
    "    img = img[np.newaxis, :]  # extend to (example, channel, heigth, width)\n",
    "    return img"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we define a input data structure which is acceptable by mxnet. The field `data` is used for the input data, which is a list of NDArrays. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from collections import namedtuple\n",
    "Batch = namedtuple('Batch', ['data'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict\n",
    "\n",
    "Now we are ready to run the prediction by `forward`. Then we can get the output using `get_outputs`, in which the i-th element is the predicted probability that the image contains the i-th class. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "truth label 577; top-1 predict label 577\n"
     ]
    }
   ],
   "source": [
    "img = get_image('val_1000/0.jpg')\n",
    "mod.forward(Batch([mx.nd.array(img)]))\n",
    "prob = mod.get_outputs()[0].asnumpy()\n",
    "y = np.argsort(np.squeeze(prob))[::-1]\n",
    "print('truth label %d; top-1 predict label %d' % (val_label[0], y[0]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When predicting more than one images, we can batch several images together which potentially improves the performance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "mod2 = mx.mod.Module(symbol=sym, context=mx.gpu())\n",
    "mod2.bind(for_training=False, data_shapes=[('data', (batch_size,3,224,224))])\n",
    "mod2.set_params(arg_params, aux_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we iterative multiple images to calculate the accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batch 0, time 0.438365 sec\n",
      "batch 1, time 0.334061 sec\n",
      "batch 2, time 0.335954 sec\n",
      "batch 3, time 0.324133 sec\n",
      "batch 4, time 0.423699 sec\n",
      "batch 5, time 0.323961 sec\n",
      "top-1 accuracy 0.677083\n"
     ]
    }
   ],
   "source": [
    "# Output may vary\n",
    "import time\n",
    "acc = 0.0\n",
    "total = 0.0\n",
    "for i in range(0, 200/batch_size):\n",
    "    tic = time.time()\n",
    "    idx = range(i*batch_size, (i+1)*batch_size)\n",
    "    img = np.concatenate([get_image('val_1000/%d.jpg'%(j)) for j in idx])\n",
    "    mod2.forward(Batch([mx.nd.array(img)]))\n",
    "    prob = mod2.get_outputs()[0].asnumpy()\n",
    "    pred = np.argsort(prob, axis=1)\n",
    "    top1 = pred[:,-1]\n",
    "    acc += sum(top1 == np.array([val_label[j] for j in idx]))\n",
    "    total += len(idx)\n",
    "    print('batch %d, time %f sec'%(i, time.time()-tic))\n",
    "assert acc/total > 0.66, \"Low top-1 accuracy.\"\n",
    "print('top-1 accuracy %f'%(acc/total))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Extract Features\n",
    "\n",
    "Sometime we want the internal outputs from a neural network rather than then final predicted probabilities. In this way, the neural network works as a feature extraction module to other applications. \n",
    "\n",
    "A loaded symbol in default only returns the last layer as output. But we can get all internal layers by `get_internals`, which returns a new symbol outputting all internal layers. The following codes print the last 10 layer names. \n",
    "\n",
    "We can also use `mx.viz.plot_network(sym)` to visually find the name of the layer we want to use. The name conventions of the output is the layer name with `_output` as the postfix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['bn1_beta',\n",
       " 'bn1_output',\n",
       " 'relu1_output',\n",
       " 'pool1_output',\n",
       " 'flatten0_output',\n",
       " 'fc1_weight',\n",
       " 'fc1_bias',\n",
       " 'fc1_output',\n",
       " 'softmax_label']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_layers = sym.get_internals()\n",
    "all_layers.list_outputs()[-10:-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Often we want to use the output before the last fully connected layers, which may return semantic features of the raw images but not too fitting to the label yet. In the ResNet case, it is the flatten layer with name `flatten0` before the last fullc layer. The following codes get the new symbol `sym3` which use the flatten layer as the last output layer, and initialize a new module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "all_layers = sym.get_internals()\n",
    "sym3 = all_layers['flatten0_output']\n",
    "mod3 = mx.mod.Module(symbol=sym3, context=mx.gpu())\n",
    "mod3.bind(for_training=False, data_shapes=[('data', (1,3,224,224))])\n",
    "mod3.set_params(arg_params, aux_params)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can do feature extraction using `forward1` as before. Notice that the last convolution layer uses 2048 channels, and we then perform an average pooling, so the output size of the flatten layer is 2048."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 2048)\n"
     ]
    }
   ],
   "source": [
    "img = get_image('val_1000/0.jpg')\n",
    "mod3.forward(Batch([mx.nd.array(img)]))\n",
    "out = mod3.get_outputs()[0].asnumpy()\n",
    "print(out.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further Readings\n"
   ]
  }
 ],
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